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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2022 / Sayı: 34

Prediction of Road Visibility Based on Meteorological Parameters by Machine Learning Methods

Makine Öğrenmesi Yöntemleri ile Meteorolojik Parametrelere Dayalı Yol Görünürlüğü Tahmini

Tahsin BAYKAL
Fatih ERGEZER
Ekinhan ERİŞKİN
Serdal TERZİ
Pages
458–462
Publication Date
2022-03-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of the evaluation, the random forest method gave the best result.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the important parameters in ensuring traffic safety is road visibility.
📊
Key Findings & Data:In the models, 96453 meteorological data sets such as temperature, humidity, wind speed, pressure, precipitation types, visibility were used between 2006 and 2016 in Szeged, Hungary.
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Conclusion & Impact:As a result of the evaluation, the random forest method gave the best result.
🔬Design:Modeling & Simulation
📍Scope:2006 Verileri
⚙️Variables:Yol güvenliği · görünürlük · makine öğrenmesi

Abstract

One of the important parameters in ensuring traffic safety is road visibility. Road visibility depends on the geometric design of the road, lighting conditions, as well as the climatic conditions in the area where the road passes. Visibility depends on meteorological parameters such as temperature, humidity, wind speed, pressure, fog, precipitation type. In this study, it is aimed to predict road visibility to ensure traffic safety. Machine learning methods were used for road visibility estimation. Machine learning models were developed with Random Forest, Extra Tree and Gradient Boosting methods. In the models, 96453 meteorological data sets such as temperature, humidity, wind speed, pressure, precipitation types, visibility were used between 2006 and 2016 in Szeged, Hungary. Developed models were evaluated with coefficient of determination (R2) and Root mean squared error (RMSE). As a result of the evaluation, the random forest method gave the best result.

Özet

Trafik güvenliğini sağlamada en önemli parametrelerden birisi yol görünürlüğüdür. Yol görünürlüğü yolun geçtiği bölgedeki iklim koşullarının yanı sıra, yolun geometric tasarımına ve aydınlatma koşullarına bağlıdır. Görünürlük, sıcaklık, nem, rüzgar hızı, basınç, sis, yağış tipi gibi meteorolojik parametrelere bağlıdır. Bu çalışmada, trafik güvenliğini sağlamak için yol görünürlük tahminlemesi yapılmıştır. Yol görünürlük tahmini için Makine öğrenme metotları kullanılmıştır. Makine öğrenme metotları Random Forest, Extra Tree ve Gradient Boosting yöntemleri ile geliştirilmiştir. Modelde, 2006-2016 yılları arasında Macaristan’ın Szeged şehrindeki sıcaklık, nem, rüzgar hızı, basınç, yağış tipi, görünürlük gibi 96453 meteorolojik data seti kullamılmıştır. Geliştirilen modeller belirtme katsayısı (R2) ve Karesel ortalama hata (KOH) ile değerlendirilmiştir. Değerlendirme sonucunda random forest methodu en iyi sonucu vermiştir.

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Cite This Article

BAYKAL, T., ERGEZER, F., ERİŞKİN, E., TERZİ, S. (2022). Prediction of Road Visibility Based on Meteorological Parameters by Machine Learning Methods. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 34, 458–462. https://ajindex.com/articles/469967

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